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Rock Blastability Evaluation based on K-Means Clustering and Entropy Weight TOPSIS Method
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Hai-wang YE1a, 1b, Bing-xiang LEI1a, Han-hong ZHOU2, Meng-hao YU1a, Tao LEI1a, 1b, Qi-zhou WANG1a, 1b, Ning LI1a, 1b, Doumbouya Sekou1a
Blasting | 2024, 41(2) : 112 - 119
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Blasting | 2024, 41(2): 112-119
BLASTING IN ORE AND ROCK
Rock Blastability Evaluation based on K-Means Clustering and Entropy Weight TOPSIS Method
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Hai-wang YE1a, 1b, Bing-xiang LEI1a, Han-hong ZHOU2, Meng-hao YU1a, Tao LEI1a, 1b, Qi-zhou WANG1a, 1b, Ning LI1a, 1b, Doumbouya Sekou1a
Affiliations
  • 1a.School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
  • 1b.Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan University of Technology, Wuhan 430070, China
  • 2.Security Administration of Wuhan Public Security Bureau, Wuhan 430077, China
Published: 2024-06-01 doi: 10.3963/j.issn.1001-487X.2024.02.014
Outline
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The distribution of blasting fragmentation in open pit mines has a direct impact on subsequent excavation, transportation, and crushing operations. To effectively control the fragmentation distribution of blasted rocks in different regions of graphite mines, a new model for evaluating rock blastability was developed using the K-means unsupervised cluster learning method and entropy weight TOPSIS evaluation method. Evaluation indexes including rock density, dynamic energy dissipation rate, dynamic compressive strength, average strain rate, and brittleness index were selected. Through entropy weight calculation, it was determined that the degree of rock breakage is most influenced by the brittleness index and least influenced by the average strain rate. The model was then applied to an actual graphite mine to assess its effectiveness. The rock blastability was divided into 10 grades based on this evaluation model. The average particle size of rocks under different grades was calculated and it was observed that as blastability grade increased, so did the average particle size. This finding demonstrates clear classification characteristics and validates the efficacy of our model. From the perspective of rock mass type of graphite ore, the rock explosibility is ranked from easy to difficult: schist, gneiss, granodiorite, mixed rock. Combined with the analysis of microscopic observation results of graphite ore, it can be seen that the lithology changes from schist to mixed rock, and the graphite crystalline content in the rock decreases, and the graphite ore explosibility grade is also higher and higher. Additionally, there exists a linear positive relationship between density/energy dissipation rate/dynamic compressive strength with rock blastability while negative correlation is observed with respect to average strain rate/brittleness index.

rock blasting  /  blastability evaluation  /  rock mechanics  /  K-means algorithm  /  entropy weight TOPSIS evaluation
Hai-wang YE, Bing-xiang LEI, Han-hong ZHOU, Meng-hao YU, Tao LEI, Qi-zhou WANG, Ning LI, Doumbouya Sekou. Rock Blastability Evaluation based on K-Means Clustering and Entropy Weight TOPSIS Method[J]. Blasting, 2024 , 41 (2) : 112 -119 . DOI: 10.3963/j.issn.1001-487X.2024.02.014
  • Hubei Province key research and development project(2021BCA152)
  • National key research and development plan project(2020YFC1909602)
Year 2024 volume 41 Issue 2
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Article Info
doi: 10.3963/j.issn.1001-487X.2024.02.014
  • Receive Date:2023-11-27
  • Online Date:2026-03-20
  • Published:2024-06-01
Article Data
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History
  • Received:2023-11-27
Funding
Hubei Province key research and development project(2021BCA152)
National key research and development plan project(2020YFC1909602)
Affiliations
    1a.School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
    1b.Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan University of Technology, Wuhan 430070, China
    2.Security Administration of Wuhan Public Security Bureau, Wuhan 430077, China

Corresponding:

LEI Tao (1983-), male, PH.D, lecturer, mainly engaged in mining, safety, numerical mining and other aspects of teaching and research, (E-mail) .
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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